In this part, we conduct experiment to verify the impact of some key components.
First, we set STEP w/o GSL to test the performance without the graph structure learning model.
Second, we set STEP w/o reg to replace the \(k\) NN graph computed by the representations of TSFormer with the \(k\) NN graph in GTS [1]}, which is computed based on the cosine similarity of raw time series \(\mathbf {S}^{i}_{train}\) , to test the superiority of the long sequence representations of TSFormer.
Finally, we also test more downstream STGNNs to verify the generality of STEP.
We choose DCRNN as another backend, i.e., STEP-DCRNN.
Additionally, we also present the performance of DCRNN for comparison.
The results are shown in Figure REF (a).
